Description
Book SynopsisThis book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You''ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.
Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classifi
Table of ContentsSection 1: Introduction to Analytics
In this section, we discuss the necessary foundations required to perform data analytics. We discuss different analytics terms, basics statistics and probability theory, descriptive statistics including various plots, and various measures for evaluating your predictive models. Chapter 1: Business Analytics Revolution
Chapter 2: Foundations of Business Analytics
Chapter 3: Structured Query Language (SQL) Analytics
Chapter 4: Business Analytics Process Chapter 5: Exploratory Data Analysis (EDA)
Chapter 6: Evaluating Analytics Model Performance
Section II: Supervised Learning and Predictive AnalyticsIn this section, we introduce statistical learning models and machine learning models. We present various regression analysis and classification analysis. We also discuss logistic regression and end our discussion by introducing Neural Network and gradient descent algorithms. Chapter 7: Simple Linear Regressions
Chapter 8: Multiple Linear Regressions
Chapter 9: Classification
Chapter 10: Neural Networks
Chapter 11: Logistic Regression
Section III: Time series modelsIn this section, we introduce optimization models and Time series analysis. In time series, we discuss different forecasting models, and in optimization models, we introduce both linear and non-linear optimization models.Chapter 12: Time Series – Forecasting
Section IV: Unsupervised model and Text MiningIn this section, we discuss two popular unsupervised models - cluster analysis and relationship data mining techniques. Finally, we end this section by introducing text mining and NLP and briefly introducing big data. Chapter 13: Cluster Analysis
Chapter 14: Relationship Data Mining
Chapter 15: Mining Text and Text Analytics Chapter 16: Big Data and Big Data Analytics
Section V: Business Analytics ToolsThis is the last part. In this section we This section summarizes what we have learned in the earlier section by working on some case studies. We work on practical cases using public datasets using both ‘R’ and ‘Python’.Chapter 17: R programming for Analytics
Chapter 18: Python Programming for Analytics
